Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA.
Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH 03756, USA.
Epigenomics. 2023 Apr;15(7):435-451. doi: 10.2217/epi-2023-0006. Epub 2023 Jun 20.
DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.
基于 DNA 甲基化(DNAm)的细胞混合物反卷积(CMD)已成为全基因组甲基化关联研究的重要组成部分,在该研究中,对异质组织类型进行 DNAm 分析。尽管该方法在十多年前就已提出,但在基于 DNAm 的 CMD 背景下,检测限(即混合生物样本中可以可靠检测到的细胞类型的最小分数)尚未确定。此外,人们很少关注用于量化基于 DNAm 的 CMD 相关不确定性的方法。本文描述了用于确定基于 DNAm 的 CMD 的细胞特异性检测限和不确定性量化的分析框架。这项工作可能有助于提高涉及 CMD 的全基因组甲基化关联研究的严谨性、可重复性和可复制性。